www.atmos-chem-phys.net/15/12211/2015/
doi:10.5194/acp-15-12211-2015
© Author(s) 2015. CC Attribution 3.0 License.
A synthesis of cloud condensation nuclei counter (CCNC) measurements within the EUCAARI network
M. Paramonov1,a, V.-M. Kerminen1, M. Gysel2, P. P. Aalto1, M. O. Andreae3, E. Asmi4, U. Baltensperger2,
A. Bougiatioti5, D. Brus4,6, G. P. Frank7, N. Good8,b, S. S. Gunthe3,c, L. Hao9, M. Irwin8,d, A. Jaatinen9, Z. Jurányi2,e, S. M. King10,f, A. Kortelainen9, A. Kristensson7, H. Lihavainen4, M. Kulmala1, U. Lohmann11, S. T. Martin10, G. McFiggans8, N. Mihalopoulos5, A. Nenes12,13,14, C. D. O’Dowd15, J. Ovadnevaite15, T. Petäjä1, U. Pöschl3,
G. C. Roberts16,17, D. Rose3,g, B. Svenningsson7, E. Swietlicki7, E. Weingartner2,e, J. Whitehead8, A. Wiedensohler18, C. Wittbom7, and B. Sierau11
1Department of Physics, University of Helsinki, P.O. Box 64, 00014 Helsinki, Finland
2Laboratory of Atmospheric Chemistry, Paul Scherrer Institute, 5232 Villigen PSI, Switzerland
3Biogeochemistry and Multiphase Chemistry Departments, Max Planck Institute for Chemistry, Mainz, Germany
4Finnish Meteorological Institute, Erik Palménin aukio 1, P.O. Box 503, 00101 Helsinki, Finland
5Environmental Chemical Processes Laboratory, University of Crete, Heraklion, Greece
6Laboratory of Aerosols Chemistry and Physics, Institute of Chemical Process Fundamentals, Academy of Sciences of the Czech Republic, Rozvojová 135, 165 02 Prague 6, Czech Republic
7Division of Nuclear Physics, Department of Physics, Lund University, P.O. Box 118, 22100 Lund, Sweden
8Centre for Atmospheric Science, SEAES, The University of Manchester, Oxford Road, Manchester M13 9PL, UK
9Department of Applied Physics, University of Eastern Finland, 70210 Kuopio, Finland
10School of Engineering and Applied Sciences and Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA 02138, USA
11Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
12School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA
13School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
14Institute of Chemical Engineering Sciences (ICE-HT), FORTH, Patras, Greece
15School of Physics and Centre for Climate and Air Pollution Studies, Ryan Institute, National University of Ireland Galway, University Road, Galway, Ireland
16Centre National de Recherches Météorologiques, Toulouse, France
17Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA 92093, USA
18Leibniz Institute for Tropospheric Research, Leipzig, Germany
anow at: Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
bnow at: Department of Mechanical Engineering, Colorado State University, Fort Collins, CO, USA
cnow at: Department of Civil Engineering, Indian Institute of Technology Madras, Chennai, India
dnow at: Cambustion Ltd., Cambridge, UK
enow at: Institute of Aerosol and Sensor Technology, University of Applied Sciences Northwestern Switzerland, Windisch, Switzerland
fnow at: Haldor Topsøe A/S, Copenhagen, Denmark
gnow at: Institute for Atmospheric and Environmental Sciences, Goethe-University Frankfurt am Main, Frankfurt am Main, Germany
Correspondence to: M. Paramonov (mikhail.paramonov@helsinki.fi), V.-M. Kerminen (veli-matti.kerminen@helsinki.fi), M. Gysel (martin.gysel@psi.ch) and B. Sierau (berko.sierau@env.ethz.ch)
Received: 30 April 2015 – Published in Atmos. Chem. Phys. Discuss.: 1 June 2015 Revised: 14 October 2015 – Accepted: 20 October 2015 – Published: 4 November 2015
Abstract. Cloud condensation nuclei counter (CCNC) mea- surements performed at 14 locations around the world within the European Integrated project on Aerosol Cloud Climate and Air Quality interactions (EUCAARI) framework have been analysed and discussed with respect to the cloud con- densation nuclei (CCN) activation and hygroscopic proper- ties of the atmospheric aerosol. The annual mean ratio of ac- tivated cloud condensation nuclei (NCCN) to the total num- ber concentration of particles (NCN), known as the activated fractionA, shows a similar functional dependence on super- saturation S at many locations – exceptions to this being certain marine locations, a free troposphere site and back- ground sites in south-west Germany and northern Finland.
The use of total number concentration of particles above 50 and 100 nm diameter when calculating the activated fractions (A50andA100, respectively) renders a much more stable de- pendence of A on S; A50 andA100 also reveal the effect of the size distribution on CCN activation. With respect to chemical composition, it was found that the hygroscopicity of aerosol particles as a function of size differs among loca- tions. The hygroscopicity parameterκ decreased with an in- creasing size at a continental site in south-west Germany and fluctuated without any particular size dependence across the observed size range in the remote tropical North Atlantic and rural central Hungary. At all other locationsκincreased with size. In fact, in Hyytiälä, Vavihill, Jungfraujoch and Pallas the difference in hygroscopicity between Aitken and accu- mulation mode aerosol was statistically significant at the 5 % significance level. In a boreal environment the assumption of a size-independentκ can lead to a potentially substantial overestimation ofNCCNatSlevels above 0.6 %. The same is true for other locations whereκ was found to increase with size. While detailed information about aerosol hygroscopic- ity can significantly improve the prediction of NCCN, total aerosol number concentration and aerosol size distribution remain more important parameters. The seasonal and diurnal patterns of CCN activation and hygroscopic properties vary among three long-term locations, highlighting the spatial and temporal variability of potential aerosol–cloud interactions in various environments.
1 Introduction
Atmospheric aerosol particles are known to modify the mi- crophysical properties of clouds, such as their albedo, life- time and precipitation patterns (Boucher et al., 2013). Due to the importance of clouds in the weather and climate systems, these aerosol-induced changes, known as the indirect effects of aerosol on climate, are a subject of rigorous research. The quantification of the radiative forcing associated with the in- teractions of atmospheric aerosol with clouds remains one of the biggest challenges in the current understanding of cli-
mate change (Boucher et al., 2013). These challenges are as- sociated with the production of the aerosol particles that are able to activate into cloud droplets, known as cloud conden- sation nuclei (CCN) (e.g. Laaksonen et al., 2005; Andreae and Rosenfeld, 2008; Kuang et al., 2009; Kerminen et al., 2012), their actual activation into cloud drops (e.g. Kulmala et al., 1996; Dusek et al., 2006; McFiggans et al., 2006; Para- monov et al., 2013; Hammer et al., 2014), the formation of clouds (e.g. Twomey, 1959; Mason and Chien, 1962; Vail- lancourt et al., 2002), time evolution of cloud microphysi- cal and other properties (e.g. Rosenfeld et al., 2014) and the interaction of clouds with the solar and terrestrial radiation (e.g. Boucher and Lohmann, 1995; Ramanathan et al., 2001;
Chen et al., 2014). A better understanding is needed with re- spect to each of these steps in order to improve the perfor- mance of the current global climate models (GCMs) and to increase the accuracy of the future climate predictions.
Several aerosol properties are of special interest when looking at the interaction of atmospheric aerosol particles with warm clouds. The current article focuses on the num- ber, size and hygroscopicity of the atmospheric aerosol parti- cles with regard to how these parameters affect the potential of particles to act as CCN. One such property of interest is the CCN number concentrationNCCN. Depending on the lo- cation,NCCN can vary by several orders of magnitude, and it directly depends on the aerosol properties and the ambi- ent water vapour supersaturation ratioS in the atmosphere.
Köhler theory dictates that the minimum size at which par- ticles activate into cloud drops decreases with increasingS (Köhler, 1936); consequentlyNCCNincreases monotonically withSfor a given aerosol population. The exact response of NCCNto an increasingSdepends on the total aerosol number concentrationNCN, aerosol size distribution and particle hy- groscopicity. Besides the relevant references found through- out the paper, discussion aboutNCCNconcentrations in var- ious environments can be found in, e.g. Pandis et al. (1994), Covert et al. (1998), Snider and Brenguier (2000), Chang et al. (2007), Andreae and Rosenfeld (2008), Andreae (2009) and Wang et al. (2010). At any givenS, another property of interest is the critical dry diameter of CCN activation Dc, defined as the smallest diameter at which particles activate into cloud drops. For internally mixed polydisperse aerosol particles, this diameter indicates that in the presence of a suf- ficient amount of water vapour all particles above this size activate into cloud drops, and all particles below this size do not. However, atmospheric aerosol is frequently externally mixed, with particles of different sizes exhibiting different chemical composition, and, therefore, in practice,Dcis usu- ally estimated as the diameter at which 50 % of the particles activate and grow into cloud drops at any givenS.Dccan be directly calculated from size-segregated cloud condensation nuclei counter (CCNC) measurements (Rose et al., 2008) or estimated from the size distribution data coupled withNCCN
(Hitzenberger et al., 2003; Furutani et al., 2008). The ef- fect of hygroscopicity on the activation of CCN into cloud drops has also been studied extensively, and several simpli- fied theoretical models have been suggested to link particle composition with critical supersaturation Sc, i.e. the mini- mum S required for the particles of a certain size to acti- vate into cloud drops (e.g. Svenningsson et al., 1992; Rissler et al., 2005; Khvorostyanov and Curry, 2007; Wex et al., 2007). One such approach is the hygroscopicity parameter κ, also known as “kappa”, a unitless number describing the cloud condensation nucleus activity (Petters and Kreiden- weis, 2007). The value of κ typically varies between zero and just above unity, with values close to zero indicating a non-hygroscopic aerosol, i.e. with low affinity for water (e.g. freshly emitted black carbon; e.g. Hudson et al., 1991;
Weingartner et al., 1997; Wittbom et al., 2014) and values close to unity indicating an aerosol with high hygroscopicity, i.e. high affinity for water (e.g. sea salt particles; e.g. Good et al., 2010). Since its introduction, the parameterκhas been used in CCN studies quite extensively (e.g. Carrico et al., 2008; Kammermann et al., 2010a; Levin et al., 2014).
This article summarises the measurements performed by CCNCs within the framework of the European Integrated project on Aerosol Cloud Climate and Air Quality interac- tions (EUCAARI). One of the EUCAARI project aims was to compile a comprehensive database of in situ measured aerosol, CCN and hygroscopic properties in order to increase the knowledge about aerosol–cloud–climate interactions and to combine the relevant existing measurement infrastructure (Kulmala et al., 2011). Besides CCNCs already deployed at the existing European long-term measurement stations, sev- eral intensive field campaigns using the CCNC were carried out as part of EUCAARI as well. The main objective of this work is to present a comprehensive overview and intercom- parison of CCNC measurements and to provide an insight into the cloud droplet activation and aerosol hygroscopic properties in different environments. More specifically, the aims are to (i) get new insight into CCN number concentra- tions and activated fractions around the world and their de- pendence on the water vapour supersaturation ratio, (ii) pro- vide new information about the dependence of aerosol hygro- scopicity on particle size, and (iii) reveal seasonal and diur- nal variation of CCN activation and hygroscopic properties.
While undeniably important, the effect of size distribution on NCCNand the size-resolved activated fraction (e.g. Dusek et al., 2006; Quinn et al., 2008; Morales Betancourt and Nenes, 2014) is not investigated herein, and an overview of the ex- isting EUCAARI aerosol size distribution data can be found in Asmi et al. (2011) and Beddows et al. (2014).
2 Methodology 2.1 Instrumentation
A CCNC is a type of instrument frequently used for studying the cloud droplet activation potential of aerosol particles. In its simplest set-up, a CCNC consists of a saturator unit and an optical particle counter (OPC) frequently running in par- allel with a condensation particle counter (CPC). For all mea- surements presented herein, the CCNC used was a commer- cially available instrument produced by Droplet Measure- ment Technologies, Inc. (DMT-CCNC), the basic principles of operation of which are described below.
Upon entering the measurement set-up, the aerosol flow is split into two sample flows, with the first flow leading to a CPC to determine the total particle number concentra- tion, hereafter referred to as NCN. The second flow feeds the aerosol into the saturator unit of the CCNC, inside of which the conditions of supersaturationSeff with respect to water vapour down the centre of the column are established.
Aerosol, flowing under laminar flow conditions, is subjected to these supersaturation conditions, during which particles with a critical supersaturationScsmaller thanSeffwill grow by the condensation of water vapour and remain in stable equilibrium, i.e. activate as CCN. The residence time inside the saturator column (∼10 s) allows for the activated parti- cles to grow to sizes larger than 1 µm in diameter; these parti- cles are then counted by the OPC providing the number con- centration of activated aerosol particles, a quantity hereafter referred to asNCCN. The described set-up is characteristic of polydisperse measurements; an inclusion of a drier, a neu- traliser and a differential mobility analyzer (DMA; Knutson and Whitby, 1975) prior to the splitting of the flow into two parallel lines allows for the selection of a particular particle size, i.e. quasi-monodisperse measurements. Such measure- ments can be performed either by varying the particle size at a constantSeff(D-scan) or by varyingSeffat a constant par- ticle size (S-scan). Such a set-up, while more complex, pro- vides activation spectra and allows for a direct calculation of the critical dry diameter of droplet activationDc (in case of theD-scan) or the critical supersaturationSc(in case of the S-scan). Typically, a CCNC operates at several different lev- els ofSeff, most commonly ranging between 0.1 and 1.0 %;
the deviations from the nominal assignedSeffvalues can be monitored and corrected by applying a standardised calibra- tion procedure, as described in Sect. 2.3. A more detailed description of the general operating procedures of the CCNC can be found in Roberts and Nenes (2005); exact details of the measurement set-up at each of the locations described in the next section can be found in the respective published lit- erature referenced throughout the text.
180° W 135° W 90° W 45° W 0° 45° E 90° E 135° E 180° E
90° S 45° S
0° 45° N
90° N
15°W
0° 15°E 30°E 45
°E
30°N 45°N
60°N 75°N
long-term site short-term campaign
long-term site and short-term campaign research cruise
Figure 1. A world map showing the locations of CCNC measurements performed during EUCAARI and presented in this study.
Table 1. Names, locations and descriptions of all measurement sites presented in the analysis.
Name of station or campaign Location Geographic coordinates Elevation Site description (m a.m.s.l.)
Hyytiälä southern Finland 61◦510N, 24◦170E 181 rural background
Vavihill southern Sweden 56◦010N, 13◦090E 172 rural background
Jungfraujoch/CLACE-6 Swiss Alps 46◦330N, 07◦590E 3580 free troposphere Mace Head west coast of Ireland 53◦190N, 09◦540W 0 coastal background
Pallas northern Finland 67◦580N, 24◦070E 560 remote background
Finokalia northern Crete 35◦200N, 25◦400E 250 remote coastal
Cabauw central Netherlands 51◦580N, 04◦560E −1 rural background
K-puszta central Hungary 46◦580N, 19◦330E 125 rural
Chilbolton southern United Kingdom 51◦090N, 01◦260W 78 continental background
COPS south-west Germany 48◦360N, 08◦120E 1156 continental background
RHaMBLe tropical North Atlantic ∼21◦N, 20◦W 0 remote marine
PRIDE-PRD2006 southeastern China 23◦330N, 113◦040E 28 rural background
AMAZE-08 northern Brazil 02◦360S, 60◦130W 108 remote background
CAREBeijing-2006 northern China 39◦310N, 116◦180E 30 suburban
2.2 Measurement sites
Data from a total of 14 EUCAARI locations have been pro- vided for this analysis, including both long-term measure- ment stations and short-term campaigns (Fig. 1). As seen in the figure, data sets came from a wide variety of loca- tions representing various environments, including marine and continental, urban and background, at altitudes ranging from the ground level to the free troposphere. The location and description of each measurement site are given in Ta- ble 1. All measurements presented herein were performed within the EUCAARI framework.
Hyytiälä Forestry Field Station in southern Finland is the location of the Station for Measuring Ecosystem–
Atmosphere Relations (SMEAR II), operated by the Univer- sity of Helsinki. Located on a flat terrain and surrounded by the boreal coniferous forest, mainly Scots pine, the station is well representative of the boreal environment (Hari and Kul- mala, 2005). It is a rural background site, with the nearest city of Tampere (population 220 000) located 50 km to the south-west. Air masses at the site can be of both Arctic and European origin; however, aerosol particle number concen- trations at this site are typically low (Sogacheva et al., 2005).
Vavihill in southern Sweden is a continental background site surrounded by grasslands and deciduous forest and op- erated by Lund University. The site is located 60–70 km NNE of the Malmö and Copenhagen urban area (population
∼2 000 000); however, it is considered to not be affected by the local anthropogenic sources (Tunved et al., 2003). Due to its location, the site is often used for monitoring the transport of pollution from continental Europe into the Nordic region (Tunved et al., 2003).
The Jungfraujoch is a high Alpine station in the Bernese Alps in Switzerland, where the aerosol measurements are performed by the Paul Scherrer Institute (PSI). Being located high in the mountains (3580 m a.s.l.), the station is far from local sources of pollution and is, in fact, in the free tropo- sphere most of the time; hence, it is considered a continen- tal background site and aerosol concentrations are very low (Collaud Coen et al., 2011). However, particularly during the summer months, the Jungfraujoch site is frequently influ- enced by the injections of more polluted air from the plan- etary boundary layer, driven by thermal convection (Jurányi et al., 2010, 2011; Kammermann et al., 2010a). The station is frequently inside clouds allowing for direct measurements of aerosol–cloud interactions.
Mace Head is a coastal marine site located on the west coast of Ireland and operated by the National University of Ireland, Galway. The distance to the nearest urban settlement of Galway City (88 km, population 65 000) renders Mace Head a clean background site; being on the coast, the station is directly exposed to the North Atlantic Ocean. Occasionally the station is subject to more polluted air masses originating from continental Europe and the United Kingdom (O’Dowd et al., 2014).
Pallas is a remote continental site in northern Finland lo- cated in the northernmost boreal forest zone in Europe; it is run by the Finnish Meteorological Institute (FMI). The sta- tion is situated on top of a treeless hill and, due to the fre- quent presence of clouds, is suitable for in situ measurements of aerosol–cloud interactions. The Pallas station is subject to both clean Arctic air masses, as well as to more polluted Eu- ropean air masses; regardless, absolute particle number con- centrations are typically low (Hatakka et al., 2003).
Finokalia station is a remote coastal site located on the island of Crete and operated by the University of Crete.
The station is located on top of a hill, and most frequently air masses arrive in Finokalia over the Mediterranean Sea (Stock et al., 2011). The station is representative of back- ground conditions as there are no local sources of pollution present; the largest nearby urban centre of Heraklion (popu- lation 175 000) is 50 km to the west.
The Cabauw Experimental Site for Atmospheric Research (CESAR) is located in the central Netherlands, 44 km from the North Sea. The station is in a rural area; however, the big cities of Utrecht and Rotterdam are nearby; the station is sub- ject to both continental and maritime conditions (Mensah et
al., 2012). The station is operated by the Royal Netherlands Meteorological Institute (KNMI).
The University of Manchester conducted four short- term measurement campaigns utilising a CCNC: K-puszta, Chilbolton, COPS and RHaMBLe. K-puszta is a rural site surrounded by deciduous–coniferous forest located on the Great Hungarian Plain in central Hungary 80 km SE of Bu- dapest. The site has no local anthropogenic pollution sources (Ion et al., 2005). Chilbolton is also a rural site, located in southern United Kingdom, 100 km WSW of London. The site is most frequently influenced by the marine air masses; a potential local source of anthropogenic pollution is the sea- sonal agricultural spraying (Campanelli et al., 2012). The Convective and Orographically-induced Precipitation Study (COPS) campaign took place at the top of the Hornisgrinde Mountain in the Black Forest region of south-west Germany.
While this site is primarily surrounded by the coniferous for- est, the close proximity to the Rhine Valley exposes the site to some anthropogenic pollution. Due to its elevation, the site is occasionally in the free troposphere (Jones et al., 2011). The Reactive Halogens in the Marine Boundary Layer (RHaM- BLe) Discovery Cruise D319 campaign was a cruise con- ducted in the tropical North Atlantic between Portugal and Cabo Verde. The operational area can be described as a re- mote marine environment with few, if any, sources of anthro- pogenic pollution. Air masses can originate from both the ocean and from the African mainland (Good et al., 2010).
The Max Planck Institute for Chemistry (MPIC) also conducted four CCNC measurement campaigns within the EUCAARI framework: PRIDE-PRD2006, AMAZE-08, CAREBeijing-2006 and CLACE-6, with the last one having taken place at the previously described Jungfraujoch station.
The PRIDE-PRD2006 campaign took place in southeastern China, in a small village∼60 km NW of Guangzhou, in the vicinity of a densely populated urban centre. The wind di- rection during the campaign rendered the site a rural recep- tor of the regional pollution originating from the Guangzhou urban cluster (Rose et al., 2010). The AMAZE-08 campaign took place at a remote site in an Amazonian rainforest, 60 km NNW of Manaus, Brazil. During the campaign, the site ex- perienced air masses characteristic of clean tropical rainfor- est conditions as well as air masses influenced by long-range transport of pollution (Gunthe et al., 2009; Martin et al., 2010). The CAREBeijing-2006 campaign was conducted at a suburban site in northern China, on the grounds of Huang Pu University in Yufa,∼50 km south of Beijing. The site is subject to air masses originating both in the south and in the north; however, being located on the outskirts of a large urban centre, particle concentrations are generally high (Gar- land et al., 2009).
2.3 Data
The measurement period for each location and a brief sum- mary of available CCNC data are presented in Fig. 2 and
2007 2008 2009 2010 2011 2012 2013 CLACE-6
CAREBeijing-2006 AMAZE-08 PRIDE-PRD2006 RHaMBLe COPS Chilbolton K-puszta Cabauw Finokalia Pallas Mace Head Jungfraujoch Vavihill Hyytiälä
Year
Figure 2. Periods of available data for all locations and campaigns.
Table 2, respectively. Available data range from mid-2006 to the end of 2012; the four long-term data sets all exceed one year in duration. As originally requested by the authors from the EUCAARI partners, some of the data were submitted in the NASA-Ames format with daily and monthly/campaign averages. Other data sets were submitted in the original time resolution and have been compiled accordingly for this overview study.
For the quality assurance of the CCNC data, data providers were requested to recalculate all values to correspond to the standard temperature and pressure and to utilise a consistent procedure for the CCNC calibration. Calibrations were asked to be performed as outlined in Rose et al. (2008) using neb- ulised, dried, charge-equilibrated and size-selected ammo- nium sulphate or sodium chloride aerosol particles. To pre- dictSeff for instrument calibration, water activity was asked to be parameterised according to either the AIM-based model (Rose et al., 2008) or the ADDEM model (Topping, 2005);
both of these models can be considered as accurate sources of water activity data, and the discussion about their associated uncertainties can be found in the corresponding references.
As none of the participating data providers noted a devia- tion from the calibration procedure, it is assumed that the data were treated accordingly. However, deviations from the described procedure and from the targetSeff levels may be possible and can potentially affect some of the conclusions presented in this paper. Uncertainties associated with devia- tions from the mentioned calibration procedure and parame- terisation are discussed in great detail in Rose et al. (2008) and Topping (2005).
For some of the polydisperse data sets, where available, differential/scanning mobility particle sizer (DMPS/SMPS;
Wang and Flagan, 1989; Wiedensohler et al., 2012) data were used in conjunction with the CCNC to derive the critical dry diameterDc. The procedure was carried out by compar- ingNCCNto the DMPS/SMPS-derived number size distribu-
tions; these were integrated from the largest size bin until the cumulativeNCNconcentration was equal toNCCN.Dc was then calculated by interpolating between the two adjacent size bins (Furutani et al., 2008). Following the calculation ofDc, the hygroscopicity parameterκwas determined using the effective hygroscopicity parameter (EH1) Köhler model (Eq. 1) assuming the surface tension of pure water (Petters and Kreidenweis, 2007; Rose et al., 2008). Due to the sur- face tension of actual cloud droplets being lower than that of pure water droplets (Facchini et al., 2000), this assumption, although commonly used, typically leads to an overestima- tion of theNCCN(Kammermann et al., 2010b).
S= D3wet−Ds3 D3wet−Ds3(1−κ)exp
4σsolMw RT ρwDwet
, (1)
whereS is water vapour saturation ratio,Dwetis the droplet diameter,Dsis the dry particle diameter, which, as per Rose et al. (2008), can be substituted withDc,κis the hygroscop- icity parameter,σsolis the surface tension of condensing so- lution (assumed to be pure water),Mwis the molar mass of water,Ris the universal gas constant,T is the absolute tem- perature andρwis the density of pure water.
For certain sites, total number concentrations of particles larger than 50 or 100 nm in diameter (N50orN100)were cal- culated from the corresponding DMPS or SMPS data.
In order to compare the results from different stations, several interpolation/extrapolation techniques were used. All NCCN concentrations were averaged for each site for each Seff level and then recalculated to correspond to the tar- getSefflevels suggested by the Aerosols, Clouds and Trace gases Research InfraStructure (ACTRIS) Network: 0.1, 0.2, 0.3, 0.5 and 1.0 %. Recalculation to the nearest target su- persaturation was accomplished by a simple linear interpo- lation/extrapolation ofNCCN as a function ofSeff using the two adjacent/nearestSeff points. For the Jungfraujoch data,
Table 2. Summary of available data for each measurement location.NCCN is the CCN number concentration,NCNis the total number concentration,Ais the activated fraction,Dcis the critical dry diameter andκis the hygroscopicity parameter. The “set-up” column indicates whether the CCNC was operating in polydisperse or monodisperse mode.Dc_calcandκ_calchave been calculated from polydisperse data using the differential/scanning mobility particle sizer (DMPS/SMPS) data.
Name of station or campaign Set-up Parameters Sefflevels Time resolution Reference Hyytiälä poly & mono NCN,NCCN,A,Dc,κ 0.0859, 0.1, 0.2,
0.216, 0.3, 0.4, 0.478, 0.5, 0.6, 0.74, 1.0, 1.26 %
original Paramonov et al. (2013)
Vavihill poly NCCN,NCN,A,Dc_calc,κ_calc 0.1, 0.2, 0.4, 0.7, 1.0 %
original Fors et al. (2011) Jungfraujoch poly NCCN,NCN,A,Dc_calc,κ_calc 0.12, 0.24, 0.35, 0.47,
0.59, 0.71, 0.83, 0.95, 1.07, 1.18 %
original Jurányi et al. (2010, 2011)
Mace Head poly NCN,NCCN,A 0.25, 0.5, 0.75 % averaged Ovadnevaite et al. (2011)
Pallas A poly NCCN,NCN,A,Dc_calc,κ_calc 0.2, 0.4, 0.6, 0.8, 1.0 %
original Jaatinen et al. (2014) Pallas B poly & mono NCN,NCCN,A,Dc,κ 0.47, 0.72, 0.97,
1.22 %
averaged (poly), original (mono)
n/a Pallas C poly & mono NCN,NCCN,A,Dc,κ 0.1, 0.15, 0.2, 0.6,
1.0 %
averaged (poly), original (mono)
Brus et al. (2013)
Finokalia A mono NCN,NCCN,Dc 0.21, 0.38, 0.52, 0.66,
0.73 %
averaged Bougiatioti et al. (2009)
Finokalia B poly NCCN,A,Dc_calc 0.21, 0.38, 0.52, 0.66,
0.73 %
averaged Bougiatioti et al. (2009)
Cabauw poly NCCN varies between 0.1
and 1.0 %
original Bègue (2012)
K-puszta mono NCCN,A,κ 0.03, 0.04, 0.10, 0.17,
0.20, 0.25, 0.44, 0.62, 0.67 %
averaged n/a
Chilbolton mono NCCN,A,Dc,κ 0.11, 0.30, 0.56,
0.94 %
averaged Whitehead et al. (2014)
COPS poly & mono NCCN,A,Dc,κ 0.11, 0.17, 0.24, 0.28,
0.32, 0.35, 0.43, 0.50, 0.65, 0.80 %
averaged Irwin et al. (2010), Jones et al. (2011), Whitehead et al. (2014)
RHaMBLe poly & mono NCCN,A,Dc,κ 0.09, 0.16, 0.29, 0.47, 0.74 %
averaged Good et al. (2010), White- head et al. (2014)
PRIDE-PRD2006 mono NCN,NCCN,A,Dc,κ 0.068, 0.27, 0.47,
0.67, 0.87, 1.27 %
original Rose et al. (2010, 2011)
AMAZE-08 mono NCN,NCCN,A,Dc,κ 0.095, 0.19, 0.28,
0.46, 0.82 %
original Gunthe et al. (2009) CAREBeijing-2006 mono NCN,NCCN,A,Dc,κ 0.066, 0.26, 0.46,
0.66, 0.86 %
original Gunthe et al. (2011)
CLACE-6 mono NCN,NCCN,A,Dc,κ 0.079, 0.17, 0.27,
0.46, 0.66 %
original Rose et al. (2013)
Dc atSeff of 0.12 and 0.95 % was recalculated to the corre- spondingDcat the targetSeffof 0.1 and 1.0 %, respectively, assuming a size-independentκ.
3 Results and discussion 3.1 CCN concentrations
Table 3 presents CCN number concentrations NCCN at all 18 measurements locations and campaigns for fiveSeff lev- els mentioned in the previous section. First and foremost, since CCN are simply a fraction of the total aerosol popu- lation with their concentration depending onSeff,NCCNval- ues atSeff of 1.0 % follow a similar pattern known from to-
tal particle number concentrations. The lowest NCCN val- ues, thus, originate in remote and clean locations, such as Pallas, the Amazonian rainforest (AMAZE-08), Jungfrau- joch and Chilbolton. The highestNCCNvalues are found in more polluted locations – CAREBeijing-2006 and PRIDE- PRD2006, both in China. At lowerSefflevels, other effects, such as those of size distribution and hygroscopicity, become more pronounced. When examiningNCCN atSeff of 0.1 %, the highest values are still found in China; similar toNCCN atSeff of 1.0 %, the lowest values are found in Pallas, the Amazonian rainforest (AMAZE-08), Jungfraujoch and also in south-west Germany (COPS).
In order to examine the CCN activation spectra in more de- tail, Fig. 3 presents cumulativeNCCN concentrations shown
Table 3. AverageNCCNconcentrations (cm−3) at all studied locations. AllNCCNconcentrations were recalculated to correspond to theSeff levels suggested by the ACTRIS network: 0.1, 0.2, 0.3, 0.5 and 1.0 %. The four long-term data sets are shown at the top of the table.
Name of station or campaign Seff=0.1 % Seff=0.2 % Seff=0.3 % Seff=0.5 % Seff=1.0 %
Vavihill 362 745 952 1285 1795
Hyytiälä 274 407 526 824 1128
Mace Head 472 526 581 691 1007
Jungfraujoch 135 249 341 444 599
PRIDE-PRD2006 1888 4594 6956 9760 13 855
CAREBeijing-2006 2547 4751 6510 8460 10 711
Cabauw 435 1607 2208 3235 6439
Finokalia B 903 1167 1431 1793 2354
Finokalia A 946 1257 1567 1882 2109
COPS 3 210 364 710 –
RHaMBLe 300 535 717 922 1153
K-puszta 146 349 512 727 834
Chilbolton 145 210 274 384 506
CLACE-6 66 126 156 205 303
Pallas B – – 149 176 247
AMAZE-08 37 85 112 136 205
Pallas C 14 38 50 74 141
Pallas A 7 19 31 50 98
0 10 20 30 40 50 60 70 80 90 100
MaceHead FinokaliaA
FinokaliaB Chilbolton
RHaMBLe Hyytiälä
CAREBeijing-2006 Jungfraujoch
CLACE-6 Vavihill
AMAZE-08 K-puszta PRIDE-PRD2006
Pallas C Pallas A
Cabauw AveragefractionofNCCNasapercentageofNCCN measuredatSeffof1.0%(%)
Seff=0.1% Seff=0.2% Seff=0.3% Seff=0.5% Seff=1.0%
1007 2109 2354 506 1153112810711599 303 1795 205 83413855 141 98 6439
Figure 3. Average cumulativeNCCNfor all available locations shown as a percentage of theNCCNmeasured at theSeffof 1.0 % (above each bar). Colours indicate the supersaturationSeffbins.
as a percentage of theNCCN measured at the highestSeffof 1.0 %. One group of locations that can be pointed out in the figure is representative of the marine environment: Finokalia, Mace Head and the RHaMBLe campaign. At these marine locations the presence of large and hygroscopic sea salt par- ticles is expected, and a large fraction of particles already activates at the lowestSeff, i.e. of the totalNCCN measured
at the highestSeff, about a third activates already at the low- estSeff. In the case of Mace Head, the observed behaviour is due to the presence of sea salt particles and a peculiar or- ganic composition of the marine aerosol (Ovadnevaite et al., 2011). Additionally, both Finokalia and Mace Head have a large fraction of the long-range transported and aged aerosol (Bougiatioti et al., 2009; Ovadnevaite et al., 2011), which has
0.2 0.4 0.6 0.8 1 1.2 0
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
SupersaturationSeff (%) ActivatedfractionA(NCCN/NCN)
overall*
Hyytiälä Vavihill Jungfraujoch Mace Head Finokalia Pallas A Pallas B Pallas C COPS RHaMBLe Pride-PRD2006 AMAZE-08 CAREBeijing-2006 CLACE-6
Figure 4. Average activated fractionAas a function of supersaturationSefffor all available data sets. Symbols represent arithmetic mean values ofAcalculated from all available data for each station for eachSefflevel. Lines represent the linear fits in the formA=a×ln(Seff)+b.
Also shown is the overall fit based on most of the data points (*Finokalia, COPS, Jungfraujoch and Pallas A, B and C data sets excluded).
The shading of the overall fit represents the prediction bounds of the fit with a confidence level of 95 %. Slope, intercept and correlation coefficient values of the linear fits can be found in Table 4.
been shown to increase particle hygroscopicity (Perry et al., 2004; Furutani et al., 2008). Chilbolton, being a continental background site representative of the regional aerosol prop- erties, also belongs to this group; however, the NCCN con- centrations at this location may be underestimated due to the aerosol not being dried prior to entering the CCNC (White- head et al., 2014).
Another group of locations with a different CCN activa- tion pattern is represented by Pallas and Cabauw – at these locations very few particles activate at the lowest Seff, and the NCCN increases drastically whenSeff changes from 0.5 to 1.0 %. This may indicate that the aerosol is dominated by the Aitken mode particles and, to a lesser extent, that the aerosol may be of low hygroscopicity. A high concentra- tion of Aitken mode particles in the autumn and low aerosol hygroscopicity in Pallas have been previously reported by Tunved et al. (2003) and Komppula et al. (2006), respec- tively. The two measurement locations discussed here are in- teresting with regard to the ratio of presumed cloud droplet number concentration (CDNC) to the total aerosol particle number concentration. It has been reported that, although under the clean and convective conditions ambient Sc may reach as high as 1.0 %, in the polluted boundary layerScusu- ally remains below 0.3 % (Ditas et al., 2012; Hammer et al., 2014; Hudson and Noble, 2014). If one assumes this value, a comparatively small fraction of aerosol in northern Finland and central Netherlands would potentially activate into cloud droplets if exposed to this Sc. This has direct implications
for the cloud formation and, thus, local climate at these loca- tions.
3.2 Activated fraction
Another variable describing CCN activation properties of an aerosol population that was examined for the majority of lo- cations is the activated fractionA calculated as a ratio of NCCNtoNCN(Fig. 4). Each activation curve in Fig. 4 is based on the arithmetic mean values ofAcalculated from all avail- able data for each station for eachSefflevel. Included in the figure is the overall fit shown with prediction bounds (95 % confidence level) based on most of the activation curves, ex- cept the outlying ones of Finokalia, COPS, Jungfraujoch and Pallas A, B and C. As can be seen in the figure from the similar shape and placement of the activation curves and in Table 4 from the similar slope and intercept values, for many locations there is no discernible difference in howAresponds to changingSeffon an annual basis; this is further signified by the prediction bounds of the overall fit. Therefore, the average total number concentrationNCN alone is sufficient in order to roughly estimate the annual meanNCCN at any givenSeff, for example, using the overall fit parameters pre- sented in Table 4. The appropriateness of the overall fit for estimatingNCCN based on NCN alone was investigated for the whole Hyytiälä data set, by comparing theNCCN mea- sured by the CCNC with theNCCNcalculated using theNCN and the overall fit presented in Table 4. Such a comparison revealed that for Hyytiälä the overall fit leads to an annual
Table 4. Parameters of the linear fit A=a×ln(Seff)+b, for all locations depicted in Fig. 4.ais the slope,bis the intercept and r is the correlation coefficient of the simple linear regression. The overall linear fit is based on most of the activation curves depicted in Fig. 4, except Finokalia, COPS, Jungfraujoch and Pallas A, B and C.
Name of station or campaign a b r
Hyytiälä 0.21 0.62 0.99
Vavihill 0.21 0.64 1.00
Jungfraujoch 0.17 0.48 1.00
Mace Head 0.23 0.79 0.98
Finokalia 0.29 0.86 0.99
Pallas A 0.08 0.19 0.99
Pallas B 0.15 0.49 0.98
Pallas C 0.13 0.35 0.98
COPS 0.31 0.92 0.97
RHaMBLe 0.21 0.70 1.00
Pride-PRD2006 0.26 0.74 0.99
AMAZE-08 0.23 0.70 0.99
CARE-Beijing2006 0.22 0.74 1.00
CLACE-6 0.22 0.69 1.00
Overall 0.22 0.69 0.96
median overestimation ofNCCNof 49, 41, 33, 17 and 2 % for theSefflevels of 0.1, 0.2, 0.3, 0.5 and 1.0 %, respectively.
ForSefflevels below 0.3 %, the variability of the overall fit, as shown by the prediction bounds, leads to the uncertainty of the predictedNCCNof up to an average of∼45 %. This un- certainty decreases exponentially forSefflevels above 0.3 %.
A global modelling study conducted by Moore et al. (2013) reported that CDNC over the continental regions is fairly in- sensitive toNCCN, where a 4–71 % uncertainty inNCCNleads to a 1–23 % uncertainty in CDNC. Since the overwhelming majority of measurements analysed in this paper were con- ducted on land, and the overall fit results in an uncertainty of the predicted annual meanNCCNof up to∼45 %, for many sites the use of the overall fit would yield a deviation of the predicted average CDNC of approximately less than 10 %.
CDNC, however, is more sensitive to NCCN in cleaner re- gions with low total particle number concentrations, such as the Alaskan Arctic and remote oceans (Moore et al., 2013).
In such areas the use of the overall fit may not be appropriate.
Four locations stand out in Fig. 4, which were not included in the overall fit.Ais visibly higher in Finokalia and during the COPS campaign than in other locations, with approx- imately 60 % of the total aerosol population at both loca- tions activating into cloud drops at theSeffof∼0.4 %. Rea- sons for the observed behaviour in Finokalia were discussed in the preceding Sect. 3.1. During the COPS campaign the size distributions varied greatly, and, as will be shown later, Aitken mode aerosol was more hygroscopic than accumu- lation mode aerosol, possibly explaining the behaviour of the COPS activation curve seen in Fig. 4 at least for higher
Seff levels (Irwin et al., 2010; Jones et al., 2011). Another location with seemingly different activation curves is Pal- las, where the activation spectrum changes throughout the year, and even at fairly highSeff level of 1.0 %, less than half of the total aerosol population activated into cloud drops.
The long-term Jungfraujoch data set also exhibited compar- atively lowAvalues, lower than those presented by Jurányi et al. (2011) and those during the CLACE-6 campaign at the same location (Fig. 4). While theAvalues in the long-term Jungfraujoch data set were calculated with respect to CPC measurements of total particle number concentration,Aval- ues for the CLACE-6 campaign and those reported by Ju- rányi et al. (2011) were calculated with respect to integrated SMPS size distribution measurements with a higher size cut- off. While the aerosol hygroscopicity at these locations will be discussed later, the effect of the size distribution on the activation curves is evident.
The similarity in howAresponds toSeffat the majority of studied locations is an interesting result. In other words, at any givenSeff the annual mean fraction of aerosol that will activate into cloud drops is pretty much the same in many locations, a fact that was pointed out previously by Andreae (2009). This phenomenon can easily be illustrated using the example of the activation curve during the RHaMBLe cruise in the tropical North Atlantic. As will be discussed later, while theNCCNhere is comparable to several other locations, the hygroscopicity of the aerosol is much higher, with the hygroscopicity parameterκbeing just below unity across all studied sizes. Yet, the fact that the aerosol is so hygroscopic seems to affect the activation efficiency of the aerosol in a similar manner as, for example, during the PRIDE-PRD2006 campaign in southeastern China. During this campaign ab- soluteNCCN was an order of magnitude higher than during the RHaMBLe cruise (Table 2), and the hygroscopicity was much lower (Rose et al., 2010). This order of magnitude dif- ference inNCCN, a large difference in κ and at least some presumed difference in the shape of size distribution between the RHaMBLe cruise and the PRIDE-PRD2006 campaign seem to result in no apparent difference in the fraction of the aerosol that activates into cloud drops at any givenSeff. For most of the continental locations the overall fit presented in Table 4 can provide a reasonable estimation of annual mean NCCNbased on theNCNfor any givenSeff. It should be kept in mind, however, that the activation curves in Fig. 4 for the long-term data sets do not reflect the potential short-term or seasonal variability, which, as can be seen in the example of the three Pallas campaigns, can be rather high. This and the fact that the short-term campaigns have been conducted dur- ing different seasons mean that the overall fit represents the annual mean activation behaviour and does not capture the variability on the shorter timescales.
One important uncertainty associated with the comparison of the activation curves in Fig. 4 is the precise size range from whichNCNis determined. In order for the activation curves to be directly comparable, the lower size limit ofNCNmust
0.2 0.4 0.6 0.8 1 1.2 1.4 10-1
100
SupersaturationSeff (%)
ActivatedfractionA
Hyytiälä,A100 Vavihill,A100 Jungfraujoch,A100 Mace Head,A100 Hyytiälä,A
50 Vavihill,A50 Jungfraujoch,A
50 Mace Head,A50
Figure 5. Average effective activated fractionsA100(NCCN/N100) andA50(NCCN/N50)as a function of supersaturationSeff for the four long-term measurement locations.
be the same for all locations. In this study, data of the lower limit ofNCNfor each location (NCN,Dmin)were unavailable and, hence, this parameter was likely to vary, complicating the comparison of activation curves in Fig. 4. To circumvent the problem, to conduct a more accurate comparison and to reveal more information about the effect of size distribution on CCN variability,N100andN50 concentrations were used instead ofNCN to calculate the effective activated fractions corresponding to a certain lower cut-off diameter A100 and A50, respectively. These were calculated for the four long- term measurement locations only (where the data were avail- able), and the results of the comparison are depicted in Fig. 5.
When N100 is used instead ofNCN, the differences among locations described above almost disappear except for the lowest values ofS. In general, the activation curve ofA100 for Mace Head is similar to those for Hyytiälä, Vavihill and Jungfraujoch forSeffabove 0.4 %. In other words, when one considers the fraction of only accumulation mode particles that activates into cloud drops at any givenSeff, the difference in howSeffaffectsAat all examined locations diminishes. In Hyytiälä, Vavihill and Jungfraujoch, particles with a dry di- ameter of 100 nm activate at theSeff of slightly higher than 0.2 % assuming an internally mixed aerosol. Around thisSeff
Mace Head does exhibit a slightly higherA100compared to other locations, possibly due to the increased CCN activity of the organically enriched Aitken mode aerosol (Ovadnevaite et al., 2011).
When A50 is examined in detail, the difference between Mace Head and other locations seen in Fig. 4 remains, with Mace Head exhibiting a higher activated fraction com- pared to the three other locations. In Hyytiälä, Vavihill and Jungfraujoch, particles with a dry diameter of 50 nm activate at aSeffof∼0.7 %, while in Mace Head these same particles activate at aSeffof∼0.55 %. Differences observed in Figs. 4 and 5 lead to the conclusion thatA50andA100have a more stable dependence onS; i.e. the variability in the fraction of
nucleation/Aitken mode particles among different locations is large. Consequently, when comparing data sets of activated fractions A from several locations with different expected concentrations of nucleation/Aitken mode particles and in- strumental set-ups, a recommendation is made for the con- sideration of usingN100 and/or N50 concentrations instead ofNCN when calculatingAcoupled withAvalues derived from total number concentrations. Besides more systematic comparison of activation curves and, therefore, more accu- rate results, such an approach can provide additional infor- mation about the effect of size distribution and its variability, and hygroscopicity on CCN activation. The use ofA100and A50also diminishes the effect of the spatial variability of the fraction of nucleation/Aitken mode particles, those less rele- vant for CCN activation at typical ambientSefflevels.
3.3 CCN and their hygroscopicity
Critical dry diameter Dc and hygroscopicity parameter κ were provided for the majority of the presented locations, and the variation ofκ with dry size is seen in Fig. 6 (the fig- ure is split into four panels for better visual representation).
The variation ofκ with dry size is not the same everywhere, and three groups can be pointed out.
In the first group of locationsκclearly increases with size;
this is the case for Hyytiälä, Vavihill, Jungfraujoch (Fig. 6, upper left panel), Pallas (Fig. 6, upper right panel), and for the four campaigns conducted by the MPIC (Fig. 6, lower right panel). At these locations accumulation mode particles have a higher hygroscopicity than the Aitken mode particles, likely due to cloud processing. The results of the Mann–
Whitney U test (Mann and Whitney, 1947) for two popu- lations that are not normally distributed (below and above 100 nm of dry size; Paramonov et al., 2013) reveal that in Hyytiälä, Vavihill, Jungfraujoch and Pallas A and C the dif- ference inκis statistically significant at the 5 % significance level; i.e. the median κ of Aitken and accumulation mode particles are significantly different (Table 5). Published data for the PRIDE-PRD2006, CAREBeijing-2006, CLACE-6 and AMAZE-08 campaigns have previously reported such a trend (Rose et al., 2010, Gunthe et al., 2011, Rose et al., 2013 and Gunthe et al., 2009, respectively). Data for Chilbolton (Fig. 6, lower left panel) also reveal an increase inκ with size, although absoluteκ values at this site may be under- estimated due to the aerosol sample not being dried before entering the CCNC (Whitehead et al., 2014). Such behaviour of κ leads to two implications. First, as already discussed in Su et al. (2010) and Paramonov et al. (2013), the hygro- scopicity of the whole aerosol population can, and in some cases should, be presented as a function of size; this can be done by way of either separateκ values for the Aitken and accumulation mode aerosol or hygroscopicity distribu- tion functions. Values ofκ derived from the CCNC are fre- quently discussed in conjunction with the chemistry infor- mation obtained, e.g. from the aerosol mass spectrometer
40 60 80 100 120 140 160 180 0
0.2 0.4 0.6 0.8 1
Hyytiälä Vavihill Jungfraujoch
50 100 150 200
0 0.2 0.4 0.6 0.8 1
Pallas A Pallas B Pallas C
50 100 150 200 250 300
0 0.2 0.4 0.6 0.8 1
Chilbolton COPS K-puszta RHaMBLe
50 100 150 200
0 0.2 0.4 0.6 0.8 1
Critical dry diameterDc (nm) Hygroscopicityparameterk
PRIDE-PRD2006 CAREBeijing-2006 AMAZE-08 CLACE-6
Figure 6. Mean hygroscopicity parameterκas a function of critical dry diameterDcfor selected locations. Figure split in four panels for more detail. Shown with one standard deviation.
Table 5. Median and percentileκvalues for Aitken (<100 nm) and accumulation (>100 nm) mode particles for Hyytiälä, Vavihill, Jungfrau- joch and Pallas A and C.
<100 nm >100 nm
Station median 25th percentile 75th percentile median 25th percentile 75th percentile
Hyytiälä 0.18 0.13 0.27 0.29 0.22 0.45
Vavihill 0.20 0.15 0.28 0.27 0.22 0.33
Jungfraujoch 0.18 0.12 0.28 0.22 0.16 0.31
Pallas A 0.09 0.07 0.13 0.13 0.09 0.20
Pallas C 0.18 0.15 0.27 0.25 0.19 0.37
(AMS) measurements. The second implication here is that if, due to instrumental limitations, such measurements are representative only of the accumulation mode particles, κ values derived from such measurements should be extended to the Aitken mode particles with caution. The effect of ex- tending the accumulation modeκ down to the Aitken mode was examined using detailed data from Hyytiälä as an ex- ample. NCCN was calculated using the median annual size distribution and Dc calculated with size-dependent and the assumed size-independent κ values. It was found that ifκ of the accumulation mode is assumed to be the same for the Aitken mode, theNCCN, on average, is overestimated by 16 and 13.5 % for theSeffof 0.6 and 1.0 %, respectively.
The second group of locations, or in this case only one location, exhibits a decrease ofκ with particle dry size, and such a trend exists only for the COPS campaign (Fig. 6, lower left panel). Apparently, at the mountainous site in the Black
Forest region of south-west Germany the chemical compo- sition of the accumulation mode aerosol makes it less hy- groscopic compared with the Aitken mode at supersaturated conditions (Irwin et al., 2010). However, the same study re- ported that the measurements by the hygroscopicity tandem DMA (HTDMA) in a sub-saturated regime revealed an in- crease ofκwith particle dry size.
The third group of locations, represented by the K-puszta station and RHaMBLe measurement campaign, is charac- terised by the absence of any dependence ofκon the particle dry size. Though quite different in magnitude (Fig. 6, lower left panel),κ values and, therefore, aerosol chemical com- position seem to have no particular size dependence across the whole measured size range. Also of interest is the high aerosol hygroscopicity across the whole investigated aerosol size range (Aitken mode) during the RHaMBLe cruise – all κvalues are just below unity (Good et al., 2010). The marine
